期刊
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
卷 58, 期 4, 页码 3283-3296出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAES.2022.3147443
关键词
Covariance matrices; Detectors; Interference; Radar detection; Radar; Optimization; Maximum likelihood estimation; Adaptive radar detection; expectation-maximization (EM) algorithm; missing data
资金
- research program PON Ricerca e Innovazione [AIM1878982-1]
This article discusses the problem of adaptive radar detection in a missing-data context and proposes the use of maximum likelihood estimation (MLE) and the expectation-maximization (EM) algorithm to handle the optimal design strategy, evaluating the performance of the designed framework.
This article deals with the problem of adaptive radar detection in a missing-data context, where the complete observations (i.e., downstream information loss mechanisms) are characterized by homogeneous Gaussian disturbance with an unknown but possibly structured covariance matrix. The detection problem, formulated as a composite hypothesis test, is tackled by resorting to suboptimal design strategies, leveraging the generalized likelihood ratio criterion demanding appropriate maximum likelihood estimates (MLEs) of the unknowns under both hypotheses. Capitalizing on some possible a priori knowledge about the interference covariance matrix structure, the optimization problems involved in the MLE computation are handled by employing the expectation-maximization (EM) algorithm or its expectation-conditional maximization and multicycle EM variants. At the analysis stage, the performance of the devised architectures is assessed both via Monte Carlo simulations and on measured data for some covariance matrix structures of practical interest.
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